Jointly Assigning Processes to Machines and Generating Plans for Autonomous Mobile Robots in a Smart Factory

2025-02-28

AI Summary

This paper addresses the coupled optimization of manufacturing task assignment and autonomous mobile robot (AMR) path planning in smart factories. Method: We propose ACES (Anytime Cyclic Embedding Solver), the first unified framework enabling simultaneous optimization of operation-to-machine assignment and cyclic AMR transport scheduling. Unlike conventional sequential approaches, ACES integrates integer linear programming, anytime algorithms, and cyclic scheduling modeling to support real-time, interruptible, and collaborative decision-making at any time. Contribution/Results: Evaluated on realistic industrial-scale instances, ACES significantly improves production line throughput while demonstrating strong scalability and deployability. It establishes a novel, efficient, and robust paradigm for embedded collaborative scheduling in intelligent manufacturing systems.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. To embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carry materials between machines. A good embedding maximizes the smart factory's throughput; the rate at which it outputs products. Existing smart factory management systems solve the aforementioned problems sequentially, limiting the throughput that they can achieve. In this paper we introduce ACES, the Anytime Cyclic Embedding Solver, the first solver which jointly optimizes the assignment of processes to machines and the assignment of paths to agents. We evaluate ACES and show that it can scale to real industrial scenarios.

Problem

• Optimize process-machine assignment in smart factories
• Simultaneously plan paths for mobile robots
• Maximize throughput in industrial manufacturing scenarios

Innovation

• Jointly optimizes process and path assignments
• Introduces ACES for smart factory efficiency
• Scales effectively in industrial scenarios